OUR COMPANY
Bring Your Ideas to Life
Everything that you dreamed of can be brought to life exactly at the moment when you decide to win.
ML — really necessary or just a trendy label?
Is ML really the right solution for my task, and will it actually help the business succeed?
“Let’s urgently implement ML!” — and suddenly a pile of money is gone for a project that hardly improved anything. Sounds familiar? In the race for efficiency and competitive advantage, it’s important to ask yourself:
Is ML really the right solution for my task, and will it actually help the business succeed?
1. Evaluate the current solution.
Sometimes it’s enough to optimize what already exists: rewrite a piece of code, reconfigure a database, or adjust a business process. Often, this delivers results faster and cheaper than building a complex ML system.
2. Will ML actually be better?
You need an honest comparison. Will the model bring a significant improvement in your key metric — prediction accuracy, speed, or profit? If the gain is minimal, maybe the game isn’t worth the candle.
3. Count the costs.
ML is not just an algorithm. It’s infrastructure, integrations, maintenance, model retraining, and team training. You need to understand how much implementation and upkeep will cost — and what the real benefit will be.
Сases
A company decided to implement ML to predict customer churn. After spending on development, training, and model support, it turned out that traditional methods delivered results faster:
Result: sales grew faster and cheaper without “smart” algorithms.
- email reminders about abandoned carts;
- ad campaign optimization based on key metrics.
Result: sales grew faster and cheaper without “smart” algorithms.
A restaurant planned to implement ML to forecast visitor flow and product purchasing. But an analysis of the current system showed it was enough
- to simply consider weather and local holidays.
Conclusion.
ML is a tool, not magic. Sometimes it can radically transform a business, and sometimes it just becomes an expensive toy. The key is not to follow hype blindly but to realistically assess costs and benefits.
ML is a tool, not magic. Sometimes it can radically transform a business, and sometimes it just becomes an expensive toy. The key is not to follow hype blindly but to realistically assess costs and benefits.